Special Issue "Computational Modeling Approaches to Finance and Fintech Innovation"

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Practice in Social Science".

Deadline for manuscript submissions: 22 September 2022 | Viewed by 5479

Special Issue Editors

Prof. Dr. Evangelos Katsamakas
E-Mail Website
Guest Editor
Gabelli School of Business, Fordham University, New York, NY 10023, USA
Interests: digital transformation; digital platforms; network effects; economics of technology; dynamics of complex systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Oleg Pavlov
E-Mail Website
Guest Editor
Worcester Polytechnic Institute, Worcester, MA 01609, USA
Interests: system dynamics; systems thinking; economics; higher education; service science

Special Issue Information

Dear Colleagues,

In recent years, we have witnessed the tremendous growth of financial and fintech innovations that are transforming financial services, financial markets and the global economy.

We invite high-quality research submissions that study all aspects of finance and fintech innovation. Methodologically, we are especially interested in computational modeling and simulation approaches, including system dynamics, agent-based modeling, network models, machine learning, natural language processing, etc. We encourage interdisciplinary research that appreciates complex systems and seeks to understand, explain, design and/or forecast system behavior. The research should have clear practical implications and it should help managers, regulators and policy-makers make better decisions and create more value, while navigating the complex fintech landscape and its implications.

A list of suggested topics includes the following:

  • Trading and algorithmic trading
  • AI/machine learning in banking
  • Blockchains and applications
  • Smart contracts
  • Investment advice and robo-advisers
  • Fintech applications
  • Payment systems
  • Bitcoin, cryptocurrencies, CBDC, digital assets, NFTs
  • Decentralized finance
  • Designing fintech products and customer experience
  • Fintech and financial markets
  • Fintech startups
  • Digitalization and Digital transformation of financial services firms and markets
  • Social media, Cloud, Mobile, IoT, AR/VR and fintech
  • Big data, predictive analytics, data visualization in financial services
  • Financial and risk analytics
  • Open banking and APIs
  • Platforms and ecosystems
  • Crowdfunding
  • P2P lending
  • Fintech and cybersecurity
  • BigTech and finance
  • Dynamics of financial instability
  • Fintech economic and social impact
  • Fintech for good (social finance, green finance, social innovation, financial inclusion, responsible investing etc.)
  • Regulation of fintech and Regtech
  • Covid-19 and fintech

We also encourage submissions on other topics related to the theme of the Special Issue.

Prof. Evangelos Katsamakas
Prof. Oleg Pavlov
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (4 papers)

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Research

Article
Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment
Systems 2022, 10(2), 24; https://doi.org/10.3390/systems10020024 - 23 Feb 2022
Viewed by 961
Abstract
Stock exchange analysis is regarded as a stochastic and demanding real-world setting in which fluctuations in stock prices are influenced by a wide range of aspects and events. In recent years, there has been a great deal of interest in social media-based data [...] Read more.
Stock exchange analysis is regarded as a stochastic and demanding real-world setting in which fluctuations in stock prices are influenced by a wide range of aspects and events. In recent years, there has been a great deal of interest in social media-based data analytics for analyzing stock exchange markets. This is due to the fact that the sentiments around major global events like Brexit or COVID-19 significantly affect business decisions and investor perceptions, as well as transactional trading statistics and index values. Hence, in this research, we examined a case study from the Brexit event to assess the influence that feelings on the subject have had on the stock markets of European Union (EU) nations. Brexit has implications for Britain and other countries under the umbrella of the European Union (EU). However, a common point of debate is the EU’s contribution preferences and benefit imbalance. For this reason, the Brexit event and its impact on stock markets for major contributors and countries with minimum donations need to be evaluated accurately. As a result, to achieve accurate analysis of the stock exchanges of different EU nations from two different viewpoints, i.e., the major contributors and countries contributing least, in response to the Brexit event, we suggest an optimal deep learning and machine learning model that incorporates social media sentiment analysis regarding Brexit to perform stock market prediction. More precisely, the machine learning-based models include support vector machines (SVM) and linear regression (LR), while convolutional neural networks (CNNs) are used as a deep learning model. In addition, this method incorporates around 1.82 million tweets regarding the major contributors and countries contributing least to the EU budget. The findings show that sentiment analysis of Brexit events using a deep learning model delivers better results in comparison with machine learning models, in terms of root mean square values (RMSE). The outcomes of stock exchange analysis for the least contributing nations in relation to the Brexit event can aid them in making stock market judgments that will eventually benefit their country and improve their poor economies. Likewise, the results of stock exchange analysis for major contributing nations can assist in lowering the possibility of loss in relation to investments, as well as helping them to make effective decisions. Full article
(This article belongs to the Special Issue Computational Modeling Approaches to Finance and Fintech Innovation)
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Article
Evolutionary Game of Small and Medium-Sized Enterprises’ Accounts-Receivable Pledge Financing in the Supply Chain
Systems 2022, 10(1), 21; https://doi.org/10.3390/systems10010021 - 17 Feb 2022
Cited by 1 | Viewed by 1013
Abstract
Due to limited guarantees, it is difficult for small and medium-sized enterprises (SMEs) to obtain loans from banks. Supply chain accounts-receivable pledge financing (SCARPF) can help in overcoming those financing difficulties. This study developed an evolutionary game model of banks, core enterprises and [...] Read more.
Due to limited guarantees, it is difficult for small and medium-sized enterprises (SMEs) to obtain loans from banks. Supply chain accounts-receivable pledge financing (SCARPF) can help in overcoming those financing difficulties. This study developed an evolutionary game model of banks, core enterprises and SMEs in SCARPF, analyzed the evolution path and evolution rules of the model, and performed a numerical simulation. The results indicated that the result of the evolutionary game depends on the initial values of the variables. When certain conditions are met, the system will evolve to (lending, keep the contract). The higher the return rate during either normal production of SMEs, the loan interest rate or supply chain punishment, the more likely it is that banks will lend money and SMEs will keep the contract. However, the bank will only be likely to lend money, enabling SMEs to keep the contract, when the probability of core enterprises and SMEs engaging in joint loan fraud—or the proportion of the benefits that SMEs share when engaging in joint loan fraud—is reduced. The results of this study provide insights for banks, core enterprises, and SMEs in supply chain financing decisions, which is conducive to solving the financing difficulties of SMEs. Full article
(This article belongs to the Special Issue Computational Modeling Approaches to Finance and Fintech Innovation)
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Article
Measuring Bank Systemic Risk in China: A Network Model Analysis
Systems 2022, 10(1), 14; https://doi.org/10.3390/systems10010014 - 08 Feb 2022
Viewed by 773
Abstract
Correlation networks and risk spillovers within financial institutions contribute to the generation and dissemination of systemic risk. In this paper, a risk correlation network is constructed among Chinese banks employing the maximum entropy method, which simulates the individual risks of banks in the [...] Read more.
Correlation networks and risk spillovers within financial institutions contribute to the generation and dissemination of systemic risk. In this paper, a risk correlation network is constructed among Chinese banks employing the maximum entropy method, which simulates the individual risks of banks in the presence of exogenous shocks, the contagious risks, and total systemic risk through the effect of network spillovers, and analyzes its influencing factors. The results show that there is an increasingly rising trend in the overall systemic risk of China’s banking industry, and that the value of systemic risk is relatively large. From the perspective of the composition of banking systemic risk, individual risk accounts for a large proportion, about 70%, which is the main source of banking systemic risk, among which China’s state-owned commercial banks are the largest source. The contagious risk of banks accounts for about 30%. Furthermore, the contagious risk contribution of various banks is basically negatively correlated with their scale. The smallest urban commercial bank in the banking industry contributes at least 50% of the contagion risk, while the state-owned commercial bank, which accounts for about 40% of the total assets of the banking industry, only contributes less than 30% of the contagion risk. Full article
(This article belongs to the Special Issue Computational Modeling Approaches to Finance and Fintech Innovation)
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Article
Startup Investment Decision Support: Application of Venture Capital Scorecards Using Machine Learning Approaches
Systems 2021, 9(3), 55; https://doi.org/10.3390/systems9030055 - 22 Jul 2021
Viewed by 1544
Abstract
This research aims to explore which kinds of metrics are more valuable in making investment decisions for a venture capital firm using machine learning methods. We measure the fit of developed companies to a venture capital firm’s investment thesis with a balanced scorecard [...] Read more.
This research aims to explore which kinds of metrics are more valuable in making investment decisions for a venture capital firm using machine learning methods. We measure the fit of developed companies to a venture capital firm’s investment thesis with a balanced scorecard based on quantitative and qualitative characteristics of the companies. Collaborating with the management team of Rose Street Capital (RSC), we explore the most influential factors of their balanced scorecard using their retrospective investment decisions of successful and failed startup companies. Our study employs six standard machine learning models and their counterparts with an additional feature selection technique. Our findings suggest that “planning strategy” and “team management” are the two most determinant factors in the firm’s investment decisions, implying that qualitative factors could be more important to startup evaluation. Furthermore, we analyzed which machine learning models were most accurate in predicting the firm’s investment decisions. Our experimental results demonstrate that the best machine learning models achieve an overall accuracy of 78% in making the correct investment decisions, with an average of 87% and 69% in predicting the decision of companies the firm would and would not have invested in, respectively. Our study provides convincing evidence that qualitative criteria could be more influential in investment decisions and machine learning models can be adapted to help provide which values may be more important to consider for a venture capital firm. Full article
(This article belongs to the Special Issue Computational Modeling Approaches to Finance and Fintech Innovation)
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